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Geospatial crop-modeling tool evolves to include corn, climate change scenarios

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Posted: July 9, 2014

In an earlier newsletter, we reported on the Production Team’s development of a new geospatial crop-modeling tool called the Geospatial Agricultural Management and Crop Assessment Framework (GAMCAF), which was used initially to quantify the region’s production capacity for a single crop: the potato. But that was just the beginning for GAMCAF. Now the researchers have incorporated a corn-crop model and climate data into the platform, allowing them to examine even more production scenarios for the region.
Potato and corn simulated yield as projected on land area basis. Left-side yield maps are current or baseline projections. Right-side yield maps are mid-century A2 climate change results for rainfed scenario only. See poster (link below) for more info.

Potato and corn simulated yield as projected on land area basis. Left-side yield maps are current or baseline projections. Right-side yield maps are mid-century A2 climate change results for rainfed scenario only. See poster (link below) for more info.

"Our long-term goal has always been to incorporate several different crop models into GAMCAF," explained Production Team member Dave Fleisher, an agricultural engineer at the USDA’s Agricultural Research Service. "Now that we’ve integrated the corn model, we can begin to compare how well various crops might perform in different areas of the region and under different management scenarios."

For example, Fleisher and his colleagues knew from their initial GAMCAF modeling work that potential for increased potato production is greater in the region’s Northern states. Now, with the integration of corn data, the researchers have demonstrated that corn is not so sensitive to latitude, with a potential for increased production that is roughly even across the region. Further, when the researchers compared the two crops in terms of their responsiveness to irrigation, they learned that the potato is more responsive than corn. Their modeling shows that potential potato yields could be increased by as much as 41% with full irrigation, while potential yield increases of irrigated corn are slightly less dramatic at 31%.

The corn model isn’t the only new addition to the GAMCAF tool. The researchers also have incorporated climate data that allow them to simulate how changes in temperature, precipitation, and atmospheric CO2 levels will affect yields for these crops. The team ran models using what is known as the A2 scenario — a plausible climate change scenario developed by the Intergovernmental Panel on Climate Change for the purpose of investigating potential outcomes of human-induced climate change. They found that under the mid-century climate conditions proposed in the A2 scenario, impacts on yields were considerable: potato yields fell by 70% of the team’s baseline projection, while corn fell by 17%.

Fleisher pointed out that these climate change simulations do more than inform us about what the future might hold for agriculture in the region; they also can help us prepare. "If we can quantify what the potential climate change impacts are, we can start to develop adaptation strategies," he said. And thanks to GAMCAF’s inclusion of management data, Fleisher and his colleagues were able to evaluate how effective various adaptation strategies might be in mitigating these hypothetical climate change-related losses. They found, for example, that increasing water availability by way of irrigation can alleviate these losses significantly for both potato and corn. Projected potato yield loss moved from -70% to -17% with the addition of irrigation, while projected corn yield moved from -17% to -3%. Adjusting planting and harvesting dates was another management strategy the team evaluated — one that they found to be potentially valuable, especially in the more northern areas of the region.

What’s next for GAMCAF? Fleisher and his colleagues are currently integrating a wheat model into the platform, and plan to add more crop models over time. This will allow them to make even more crop-by-crop comparisons and ultimately may help to identify potential land-use reconfigurations that would better meet the food needs of the region.

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